cad-based-geometry.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a9974ea-bc01-43a5-85e1-aab28239bcbb",
   "metadata": {},
   "source": [
    "# DAGMC's CAD-Based Geometry in OpenMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74bf83ba-8f19-48f5-bda7-f570891157c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import openmc\n",
    "import math\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83236272-dbd7-48fd-9705-3344dd15415d",
   "metadata": {},
   "source": [
    "## Warning\n",
    "\n",
    "This notebook requires a version of OpenMC built with DAGMC geometry enabled. Use the following cell to determine whether or not this feature is enabled for the version of OpenMC being used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10b1db49-6ea9-45a5-853d-b4e82ed59f0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from warnings import warn\n",
    "import openmc.lib\n",
    "\n",
    "if not openmc.lib._dagmc_enabled():\n",
    "    warn(\"DAGMC is not enabled.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9410e2fc-c606-4527-a453-fb11e1f29010",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "\n",
    "fuel_pin_url = 'https://tinyurl.com/y3ugwz6w' # 1.2 MB\n",
    "teapot_url = 'https://tinyurl.com/y4mcmc3u' # 29 MB\n",
    "paramak_w_graveyard = 'https://tinyurl.com/y7w238th' # 18 MB\n",
    "\n",
    "def download(url, filename=\"dagmc.h5m\"):\n",
    "    \"\"\"\n",
    "    Helper function for retrieving dagmc models\n",
    "    \"\"\"\n",
    "    u = urllib.request.urlopen(url)\n",
    "    \n",
    "    if u.status != 200:\n",
    "        raise RuntimeError(\"Failed to download file.\")\n",
    "    \n",
    "    # save file as dagmc.h5m\n",
    "    with open(filename, 'wb') as f:\n",
    "        f.write(u.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c6e2ca-d588-4d04-b00c-0ce44a0427e7",
   "metadata": {},
   "source": [
    "To start, we'll be using a simple U235 fuel pin surrounded by a water moderator, so let's create those materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fb13df7-df2e-4919-be9d-290c79f8dfd0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    " # materials\n",
    "u235 = openmc.Material(name=\"fuel\")\n",
    "u235.add_nuclide('U235', 1.0, 'ao')\n",
    "u235.set_density('g/cc', 11)\n",
    "u235.id = 40\n",
    "\n",
    "water = openmc.Material(name=\"water\")\n",
    "water.add_nuclide('H1', 2.0, 'ao')\n",
    "water.add_nuclide('O16', 1.0, 'ao')\n",
    "water.set_density('g/cc', 1.0)\n",
    "water.add_s_alpha_beta('c_H_in_H2O')\n",
    "water.id = 41\n",
    "\n",
    "materials = openmc.Materials([u235, water])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf215b70-c6fb-4580-890e-1a8eb156431b",
   "metadata": {},
   "source": [
    "Now let's get our DAGMC geometry. We'll be using prefabricated models in this notebook. For information on how to create your own DAGMC models, you can refer to the instructions [here](https://svalinn.github.io/DAGMC/usersguide/trelis_workflow.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25a3701-c881-49c9-8cdd-498559ec39eb",
   "metadata": {},
   "source": [
    "Let's download the DAGMC model. These models come in the form of triangle surface meshes stored using the the Mesh Oriented datABase ([MOAB](https://sigma.mcs.anl.gov/moab-library/)) in an HDF5 file with the extension `.h5m`. An example of a coarse triangle mesh looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00f18623-45ab-43a2-b42e-82eb8a23600c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "width": 350
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"./images/cylinder_mesh.png\", width=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6718409f-3dad-40ba-b813-45e2f74b5c17",
   "metadata": {},
   "source": [
    "First we'll need to grab some pre-made DagMC models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e064e35-eca6-48e7-bbdf-cd821981dc58",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "download(fuel_pin_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafc3793-55e5-4d55-adda-3bf7f84ca6ea",
   "metadata": {},
   "source": [
    "To create a geometry where DAGMC represents the entire model, we'll make a DAGMC universe and use it as the root universe of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59e31d21-c3d3-40a4-ba24-f06d0236908c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dagmc_univ = openmc.DAGMCUniverse(filename=\"dagmc.h5m\")\n",
    "geometry = openmc.Geometry(root=dagmc_univ)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd42ba6-b0f0-4744-a5b9-847ee58b91ef",
   "metadata": {},
   "source": [
    "Now we'll tie the geometry, materials and some settings into a model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e4d77a4-9d63-4e1d-8bf4-412d0d6118b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "settings = openmc.Settings()\n",
    "settings.batches = 10\n",
    "settings.inactive = 2\n",
    "settings.particles = 5000\n",
    "\n",
    "model = openmc.Model(geometry=geometry, materials=materials, settings=settings)\n",
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d60bf50d-5a10-405e-ab31-fb8953a0b88b",
   "metadata": {},
   "source": [
    "Unlike conventional geometries in OpenMC, we really have no way of knowing what our model looks like at this point. Thankfully DagMC geometries can be plotted just like any other OpenMC geometry to give us an idea of what we're now working with.\n",
    "\n",
    "Note that material assignments have already been applied to this model. Materials can be assigned either using ids or names of materials in the `materials.xml` file. It is recommended that material names are used for assignment for readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c68dcf27-016d-4261-b440-d5af5fc0cf09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = openmc.Plot()\n",
    "p.width = (25.0, 25.0)\n",
    "p.pixels = (400, 400)\n",
    "p.color_by = 'material'\n",
    "p.colors = {u235: 'yellow', water: 'blue'}\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b853e83-0d3f-4e88-bb55-eaa9e7619b49",
   "metadata": {},
   "source": [
    "Now that we've had a chance to examine the model a bit, we can finish applying our settings and add a source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6074922-b10d-41a4-b11d-f12cff81af45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.settings.source = openmc.IndependentSource(space=openmc.stats.Box([-4., -4., -4.],\n",
    "                                                       [ 4.,  4.,  4.]))\n",
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0e849e-582f-46e0-bf35-3e49e7f3152b",
   "metadata": {},
   "source": [
    "Tallies work in the same way when using DAGMC geometries too. We'll add a tally on the fuel cell here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f133d2b8-5328-48f8-9b50-df923d60f276",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tally = openmc.Tally()\n",
    "tally.scores = ['total']\n",
    "tally.filters = [openmc.CellFilter(1)]\n",
    "tallies = openmc.Tallies([tally])\n",
    "\n",
    "model.tallies = tallies\n",
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b77de1-3df3-4616-b808-f48b7ab1af83",
   "metadata": {},
   "source": [
    "**Note:** Applying tally filters in DagMC models requires prior knowledge of the model. Here, we know that the fuel cell's volume ID in the CAD sofware is 1. To identify cells without use of CAD software, load them into the [OpenMC plotter](https://github.com/openmc-dev/plotter) where cell, material, and volume IDs can be identified for native both OpenMC and DagMC geometries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce815a75-28ac-4695-90fb-bf367059a5b0",
   "metadata": {},
   "source": [
    "Now we're ready to run the simulation just like any other OpenMC run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec67ff63-a9e7-4d74-9a4e-ec9383eca317",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | c7c39e10c249c7c8eec7b2b69438cfddaa0c6b27\n",
      "       Date/Time | 2023-06-01 16:14:22\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file dagmc.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 0.0001\n",
      "Building acceleration data structures...\n",
      " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n",
      " Reading c_H_in_H2O from /home/pshriwise/data/xs/openmc/nndc_hdf5/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for U235\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    1.16613\n",
      "        2/1    1.06031\n",
      "        3/1    1.00697\n",
      "        4/1    0.99734    1.00215 +/- 0.00482\n",
      "        5/1    0.96936    0.99122 +/- 0.01128\n",
      "        6/1    0.99125    0.99123 +/- 0.00798\n",
      "        7/1    0.94314    0.98161 +/- 0.01143\n",
      "        8/1    0.97365    0.98028 +/- 0.00943\n",
      "        9/1    0.94106    0.97468 +/- 0.00974\n",
      "       10/1    0.95085    0.97170 +/- 0.00895\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 7.7672e-02 seconds\n",
      "   Reading cross sections          = 5.3137e-02 seconds\n",
      " Total time in simulation          = 1.8259e-01 seconds\n",
      "   Time in transport only          = 1.7760e-01 seconds\n",
      "   Time in inactive batches        = 3.6324e-02 seconds\n",
      "   Time in active batches          = 1.4627e-01 seconds\n",
      "   Time synchronizing fission bank = 2.6427e-03 seconds\n",
      "     Sampling source sites         = 2.3146e-03 seconds\n",
      "     SEND/RECV source sites        = 3.2564e-04 seconds\n",
      "   Time accumulating tallies       = 1.7070e-05 seconds\n",
      "   Time writing statepoints        = 1.7707e-03 seconds\n",
      " Total time for finalization       = 5.8761e-05 seconds\n",
      " Total time elapsed                = 2.6303e-01 seconds\n",
      " Calculation Rate (inactive)       = 275299 particles/second\n",
      " Calculation Rate (active)         = 273466 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 0.96806 +/- 0.00845\n",
      " k-effective (Track-length)  = 0.97170 +/- 0.00895\n",
      " k-effective (Absorption)    = 0.95930 +/- 0.00976\n",
      " Combined k-effective        = 0.96638 +/- 0.01025\n",
      " Leakage Fraction            = 0.57688 +/- 0.00444\n",
      "\n"
     ]
    }
   ],
   "source": [
    "openmc.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4f07a6-fb83-4525-98d7-24c27a587061",
   "metadata": {},
   "source": [
    "Neat! But this pincell is something we could've done with CSG. Let's take a look at something more complex. We'll download a pre-built model of the [Utah teapot](https://en.wikipedia.org/wiki/Utah_teapot) and use it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85d416c2-ca27-43d9-8707-f79eea64dcca",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(teapot_url, \"teapot.h5m\")\n",
    "dagmc_univ = openmc.DAGMCUniverse(filename=\"teapot.h5m\")\n",
    "geometry = openmc.Geometry(root=dagmc_univ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "728604d4-c898-465c-a5c6-aa47542fdc8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/jpeg": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"./images/teapot.jpg\", width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9928c6-c4c5-47c3-842f-84111e77fa74",
   "metadata": {},
   "source": [
    "Our teapot is made out of iron, so we'll want to create that material and make sure it is in our `materials.xml` file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1d1375",
   "metadata": {},
   "source": [
    "The DAGMC file contains tessellated surfaces that make up volumes. We can use built-in methods to inspect the geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44690ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns the number of cells in the dagmc geometry\n",
    "dagmc_univ.n_cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2911de4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns the number of surfaces in the dagmc geometry\n",
    "dagmc_univ.n_surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a83ed7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Graveyard', 'iron', 'water']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns the names of materials used in the geometry.\n",
    "# these names are used when creating openmc.Materials\n",
    "dagmc_univ.material_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5548a539-f133-44a2-8450-89c859360f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pshriwise/soft/openmc/openmc/mixin.py:70: IDWarning: Another Material instance already exists with id=41.\n",
      "  warn(msg, IDWarning)\n"
     ]
    }
   ],
   "source": [
    "iron = openmc.Material(name=\"iron\")\n",
    "iron.add_nuclide(\"Fe54\", 0.0564555822608)\n",
    "iron.add_nuclide(\"Fe56\", 0.919015287728)\n",
    "iron.add_nuclide(\"Fe57\", 0.0216036861685)\n",
    "iron.add_nuclide(\"Fe58\", 0.00292544384231)\n",
    "iron.set_density(\"g/cm3\", 7.874)\n",
    "\n",
    "water = openmc.Material(name=\"water\")\n",
    "water.add_nuclide('H1', 2.0, 'ao')\n",
    "water.add_nuclide('O16', 1.0, 'ao')\n",
    "water.set_density('g/cc', 1.0)\n",
    "water.add_s_alpha_beta('c_H_in_H2O')\n",
    "water.id = 41\n",
    "\n",
    "materials = openmc.Materials([iron, water])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8f4dd2d-667a-40f9-b867-23776b08943e",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = openmc.Settings()\n",
    "settings.batches = 10\n",
    "settings.particles = 5000\n",
    "settings.run_mode = \"fixed source\"\n",
    "\n",
    "space = openmc.stats.Box(*geometry.bounding_box)\n",
    "my_source = openmc.IndependentSource(space=space, domains=[water])\n",
    "my_source.energy = openmc.stats.Discrete(x=[12.0,], p=[1.0,])\n",
    "\n",
    "settings.source = my_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "403597b3-e66e-4ab2-bab2-f0b940704de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = openmc.RegularMesh()  # could use from_domain here to avid setting lower_left and upper_right\n",
    "mesh.dimension = (120, 1, 40)\n",
    "mesh.lower_left = (-20.0, 0.0, -10.0)\n",
    "mesh.upper_right = (20.0, 1.0, 4.0)\n",
    "\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "\n",
    "pot_filter = openmc.CellFilter([1])\n",
    "pot_tally = openmc.Tally()\n",
    "pot_tally.filters = [mesh_filter, pot_filter]\n",
    "pot_tally.scores = ['flux']\n",
    "\n",
    "water_filter = openmc.CellFilter([5])\n",
    "water_tally = openmc.Tally()\n",
    "water_tally.filters = [mesh_filter, water_filter]\n",
    "water_tally.scores = ['flux']\n",
    "\n",
    "\n",
    "tallies = openmc.Tallies([pot_tally, water_tally])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "67630d35-6f83-4993-a286-d68f570702df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = openmc.Model(materials=materials, geometry=geometry, settings=settings, tallies=tallies)\n",
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a00180de-8121-4a97-a296-9587cceb0fba",
   "metadata": {},
   "source": [
    "To make sure we've updated the file correctly, let's make a plot of the teapot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c6c58151-4b6c-4e3d-92d9-e657c2c239c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = openmc.Plot()\n",
    "p.basis = 'xz'\n",
    "p.origin = (0.0, -0.1, 0.0)\n",
    "p.width = (30.0, 20.0)\n",
    "p.pixels = (450, 300)\n",
    "p.color_by = 'material'\n",
    "p.colors = {iron: 'gray', water: 'blue'}\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7256bd2-d743-4e7b-aee7-67a4d3e13e2f",
   "metadata": {},
   "source": [
    "Here we start to see some of the advantages CAD geometries provide. This particular file was pulled from the [GrabCAD](https://grabcad.com/library) and pushed through the DAGMC workflow without modification (other than the addition of material assignments). It would take a considerable amount of time to create a model like this using CSG!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d7b58fe-1cfb-4913-b979-67e499e0301d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.width = (18.0, 6.0)\n",
    "p.basis = 'xz'\n",
    "p.origin = (10.0, 0.1, 5.0)\n",
    "p.pixels = (600, 200)\n",
    "p.color_by = 'material'\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff73df08-791a-4df6-8e9c-9eea2aaefd7d",
   "metadata": {},
   "source": [
    "Now let's brew some tea! ... using a very hot neutron source. We'll use some well-placed point sources distributed throughout the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee598db0-2219-4122-b58a-5996aa74ffac",
   "metadata": {},
   "source": [
    "...and setup a couple of mesh tallies. One for the kettle, and one for the water inside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "30d37b8d-4e2d-4117-8226-58b244226c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | c7c39e10c249c7c8eec7b2b69438cfddaa0c6b27\n",
      "       Date/Time | 2023-06-01 16:14:26\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file teapot.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 0.001\n",
      "Building acceleration data structures...\n",
      " Reading Fe54 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe58.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n",
      " Reading c_H_in_H2O from /home/pshriwise/data/xs/openmc/nndc_hdf5/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for Fe58\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 5.2618e-01 seconds\n",
      "   Reading cross sections          = 2.1726e-01 seconds\n",
      " Total time in simulation          = 3.1593e+00 seconds\n",
      "   Time in transport only          = 3.1571e+00 seconds\n",
      "   Time in active batches          = 3.1593e+00 seconds\n",
      "   Time accumulating tallies       = 1.4655e-04 seconds\n",
      "   Time writing statepoints        = 1.9288e-03 seconds\n",
      " Total time for finalization       = 6.6748e-03 seconds\n",
      " Total time elapsed                = 3.6930e+00 seconds\n",
      " Calculation Rate (active)         = 15826.2 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.69476 +/- 0.00215\n",
      "\n"
     ]
    }
   ],
   "source": [
    "openmc.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4a2a28-24fe-4c08-8371-e3062b0451b6",
   "metadata": {},
   "source": [
    "Note that the performance is significantly lower than our pincell model due to the increased complexity of the model, but it allows us to examine tally results like these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22771d2e-9ba8-41a0-9444-9bebc2bc3d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = openmc.StatePoint(\"statepoint.10.h5\")\n",
    "\n",
    "water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n",
    "water_flux = water_tally.mean\n",
    "water_flux.shape = (40, 120)\n",
    "water_flux = water_flux[::-1, :]\n",
    "\n",
    "pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n",
    "pot_flux = pot_tally.mean\n",
    "pot_flux.shape = (40, 120)\n",
    "pot_flux = pot_flux[::-1, :]\n",
    "\n",
    "sp.close() # close the statepoint file to release HDF5 file handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "34aa4367-c6e5-480b-b46b-96894c29c85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0f8b4e00a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(18, 16))\n",
    "\n",
    "sub_plot1 = plt.subplot(121, title=\"Kettle Flux\")\n",
    "sub_plot1.imshow(pot_flux)\n",
    "\n",
    "sub_plot2 = plt.subplot(122, title=\"Water Flux\")\n",
    "sub_plot2.imshow(water_flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51adb2b9-3525-4672-97b3-ac827f45d5db",
   "metadata": {},
   "source": [
    "# Tokamak Geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bbebc3-e575-4043-a7df-027f5f4e4fde",
   "metadata": {},
   "source": [
    "Next we'll look at a tokamak model courtesy of John Shimwell's [paramak](https://github.com/fusion-energy/paramak) package. With this model, we'll explore how to use CAD geometry as a universe in OpenMC to create repeating instances complex geometry or clip a CAD model to insert it into an OpenMC CSG geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99cf68d1-d13f-44fe-bbca-5a33d0d5957d",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(paramak_w_graveyard, 'paramak_with_graveyard.h5m')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c05698-1059-401d-bff9-f2a31903be7f",
   "metadata": {},
   "source": [
    "First, we'll create some materials that we know are present in the paramak model. These will be assigned by the `Material.name` attribute to match those in the `paramak_with_graveyard.h5m` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "694f7b47-aab1-44e8-a516-8fa498332643",
   "metadata": {},
   "outputs": [],
   "source": [
    "materials = openmc.Materials()\n",
    "\n",
    "mat_pf_coil_1 = openmc.Material(name=\"pf_coil_1\")\n",
    "mat_pf_coil_1.add_element(\"Cu\", 1, \"ao\")\n",
    "mat_pf_coil_1.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_1)\n",
    "\n",
    "mat_pf_coil_2 = openmc.Material(name=\"pf_coil_2\")\n",
    "mat_pf_coil_2.add_element(\"Cu\", 1, \"ao\")\n",
    "mat_pf_coil_2.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_2)\n",
    "\n",
    "mat_pf_coil_3 = openmc.Material(name=\"pf_coil_3\")\n",
    "mat_pf_coil_3.add_element(\"Cu\", 1, \"ao\")\n",
    "mat_pf_coil_3.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_3)\n",
    "\n",
    "mat_pf_coil_4 = openmc.Material(name=\"pf_coil_4\")\n",
    "mat_pf_coil_4.add_element(\"Cu\", 1, \"ao\")\n",
    "mat_pf_coil_4.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_4)\n",
    "\n",
    "mat_plasma = openmc.Material(name=\"plasma\")\n",
    "mat_plasma.add_element(\"H\", 1, \"ao\")\n",
    "mat_plasma.set_density(\"g/cm3\", 0.00001)\n",
    "materials.append(mat_plasma)\n",
    "\n",
    "mat_inboard_tf_coils = openmc.Material(name=\"inboard_tf_coils\")\n",
    "mat_inboard_tf_coils.add_element(\"Cu\", 1, \"ao\")\n",
    "mat_inboard_tf_coils.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_inboard_tf_coils)\n",
    "\n",
    "mat_center_column_shield = openmc.Material(name=\"center_column_shield\")\n",
    "mat_center_column_shield.add_element(\"W\", 1, \"ao\")\n",
    "mat_center_column_shield.set_density(\"g/cm3\", 19.3)\n",
    "materials.append(mat_center_column_shield)\n",
    "\n",
    "\n",
    "mat_firstwall = openmc.Material(name=\"firstwall\")\n",
    "mat_firstwall.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_firstwall.set_density(\"g/cm3\", 7.7)\n",
    "materials.append(mat_firstwall)\n",
    "\n",
    "mat_blanket = openmc.Material(name=\"blanket\")\n",
    "mat_blanket.add_elements_from_formula(\"Pb842Li158\")\n",
    "mat_blanket.set_density(\"g/cm3\", 19.)\n",
    "materials.append(mat_blanket)\n",
    "\n",
    "mat_blanket_rear_wall = openmc.Material(name=\"blanket_rear_wall\")\n",
    "mat_blanket_rear_wall.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_blanket_rear_wall.set_density(\"g/cm3\", 7.7)\n",
    "materials.append(mat_blanket_rear_wall)\n",
    "\n",
    "mat_divertor_upper = openmc.Material(name=\"divertor_upper\")\n",
    "mat_divertor_upper.add_element(\"W\", 1, \"ao\")\n",
    "mat_divertor_upper.set_density(\"g/cm3\", 19.3)\n",
    "materials.append(mat_divertor_upper)\n",
    "\n",
    "mat_divertor_lower = openmc.Material(name=\"divertor_lower\")\n",
    "mat_divertor_lower.add_element(\"W\", 1, \"ao\")\n",
    "mat_divertor_lower.set_density(\"g/cm3\", 19.3)\n",
    "materials.append(mat_divertor_lower)\n",
    "\n",
    "mat_pf_coil_case_1 = openmc.Material(name=\"pf_coil_case_1\")\n",
    "mat_pf_coil_case_1.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_pf_coil_case_1.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_case_1)\n",
    "\n",
    "mat_pf_coil_case_2 = openmc.Material(name=\"pf_coil_case_2\")\n",
    "mat_pf_coil_case_2.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_pf_coil_case_2.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_case_2)\n",
    "\n",
    "mat_pf_coil_case_3 = openmc.Material(name=\"pf_coil_case_3\")\n",
    "mat_pf_coil_case_3.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_pf_coil_case_3.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_case_3)\n",
    "\n",
    "mat_pf_coil_case_4 = openmc.Material(name=\"pf_coil_case_4\")\n",
    "mat_pf_coil_case_4.add_element(\"Fe\", 1, \"ao\")\n",
    "mat_pf_coil_case_4.set_density(\"g/cm3\", 8.96)\n",
    "materials.append(mat_pf_coil_case_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34130f7f-747d-4b0d-98e5-14833890cc76",
   "metadata": {},
   "source": [
    "Next, we'll create a ring source to mock a fusion neutron source in the middle of the tokamak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4622477a-32e1-4626-9643-898e5c845169",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pshriwise/soft/openmc/openmc/stats/univariate.py:757: FutureWarning: The Muir(...) class has been replaced by the muir(...) function and will be removed in a future version of OpenMC. Use muir(...) instead.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "# creates a simple isotropic neutron source in the center with 14MeV neutrons\n",
    "source = openmc.IndependentSource()\n",
    "# the distribution of radius is just a single value at the plasma major radius\n",
    "radius = openmc.stats.Discrete([250.], [1])\n",
    "# the distribution of source z values is just a single value\n",
    "z_values = openmc.stats.Discrete([0], [1])\n",
    "# the distribution of source azimuthal angles values is a uniform distribution between 0 and 0.5 Pi\n",
    "# these angles must be the same as the reflective angles\n",
    "angle = openmc.stats.Uniform(a=0., b=math.radians(180))\n",
    "# this makes the ring source using the three distributions and a radius\n",
    "source.space = openmc.stats.CylindricalIndependent(r=radius, phi=angle, z=z_values, origin=(0.0, 0.0, 0.0))\n",
    "# sets the direction to isotropic\n",
    "source.angle = openmc.stats.Isotropic()\n",
    "# sets the energy distribution to a Muir distribution of D-T fusion neutrons\n",
    "source.energy = openmc.stats.muir(e0=14_000_000.0, m_rat=5.0, kt=20000.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3d10c5-27e4-48de-9fdc-6577eedd9bb1",
   "metadata": {},
   "source": [
    "Now we'll create a CAD-based DAGMC universe containing the paramak model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3682d2d2-728c-44e2-b8be-d73988355ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dag_universe = openmc.DAGMCUniverse('paramak_with_graveyard.h5m')\n",
    "geometry = openmc.Geometry(root=dag_universe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85ad735-714f-48f4-98da-5d34bcd4c226",
   "metadata": {},
   "source": [
    "And we'll apply a flux tally using a mesh over the entire model that tallies flux results only in the blanket region (shown in . Access to information about DAGMC models from the Python API is limited to information like the global bounding box of the model, which is enough to use the `RegularMesh.from_domain` method to automatically create a mesh that covers the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2de1ee10-b604-42a2-8dfa-a6c514a0b95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = openmc.RegularMesh.from_domain(geometry, dimension=(100,100,100))\n",
    "\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "\n",
    "material_filter = openmc.MaterialFilter([mat_blanket])\n",
    "\n",
    "flux_tally = openmc.Tally(name='blanket flux')\n",
    "flux_tally.filters = [mesh_filter, material_filter]\n",
    "flux_tally.scores = ['flux']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7695ccb6-55c6-480b-b1ad-219e8c3086a7",
   "metadata": {},
   "source": [
    "We'll now tie the materials, geometry, tallies, and settings together in a model class and plot the model. Below is an X-Z slice plot of this model colored by material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c86d959-6c4f-41fd-8897-65d3db47e635",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = openmc.Model()\n",
    "model.geometry = geometry\n",
    "model.materials = materials\n",
    "model.tallies = [flux_tally]\n",
    "model.settings.run_mode = 'fixed source'\n",
    "model.settings.source = source\n",
    "model.settings.batches = 10\n",
    "model.settings.particles = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9189808d-2dc8-47ca-9c17-728f2793b53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "93f8e38c-2db2-41a0-a92c-4e42330483e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = openmc.Plot()\n",
    "p.origin = (0, 10, 0)\n",
    "p.pixels = (800, 800)\n",
    "p.width = (1200, 1200)\n",
    "p.basis = 'xz'\n",
    "p.color_by = 'material'\n",
    "openmc.plot_inline([p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "065e4223-1975-4d97-9d34-492ceb963e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.basis = 'xy'\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba560af5-11b7-4be0-a75a-cb1d73632b76",
   "metadata": {},
   "source": [
    "Time to run a full simulation so we can view the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8eeb0c31-ef09-499e-9358-9df103c2db2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | c7c39e10c249c7c8eec7b2b69438cfddaa0c6b27\n",
      "       Date/Time | 2023-06-01 16:14:35\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file paramak_with_graveyard.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 4.65383e-310\n",
      "Building acceleration data structures...\n",
      " Reading Cu63 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu63.h5\n",
      " Reading Cu65 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu65.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading H2 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H2.h5\n",
      " Reading W180 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W180.h5\n",
      " Reading W182 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W182.h5\n",
      " Reading W183 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W183.h5\n",
      " Reading W184 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W184.h5\n",
      " Reading W186 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W186.h5\n",
      " Reading Fe54 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe58.h5\n",
      " Reading Pb204 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb204.h5\n",
      " Reading Pb206 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb206.h5\n",
      " Reading Pb207 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb207.h5\n",
      " Reading Pb208 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb208.h5\n",
      " Reading Li6 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li6.h5\n",
      " Reading Li7 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li7.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for H1\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.0842e+00 seconds\n",
      "   Reading cross sections          = 8.7142e-01 seconds\n",
      " Total time in simulation          = 6.8922e+00 seconds\n",
      "   Time in transport only          = 6.8668e+00 seconds\n",
      "   Time in active batches          = 6.8922e+00 seconds\n",
      "   Time accumulating tallies       = 1.0196e-02 seconds\n",
      "   Time writing statepoints        = 1.5014e-02 seconds\n",
      " Total time for finalization       = 6.8328e-01 seconds\n",
      " Total time elapsed                = 8.6707e+00 seconds\n",
      " Calculation Rate (active)         = 14509.2 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.81232 +/- 0.00226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sp_filename = model.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "972141aa-5e35-45e0-99bb-19f216eac372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0f80d58c70>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with openmc.StatePoint(sp_filename) as sp:\n",
    "    flux_tally = sp.get_tally(name='blanket flux')\n",
    "\n",
    "flux_mean = flux_tally.mean.reshape(*mesh.dimension)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(flux_mean[:, 50, :], origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486659bd-888c-4272-a2f2-555f9ee6f001",
   "metadata": {},
   "source": [
    "Let's change this tokamak model from a 180 section to a 90 degree section by creating a CSG box and filling it with the DAGMC universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "92e746c7-fd06-4733-a736-c747bac40fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min = openmc.XPlane(0.0, boundary_type='reflective')\n",
    "x_max = openmc.XPlane(600.0, boundary_type='vacuum')\n",
    "\n",
    "y_min = openmc.YPlane(0.0, boundary_type='reflective')\n",
    "y_max = openmc.YPlane(600.0, boundary_type='vacuum')\n",
    "\n",
    "region = +x_min & -x_max & +y_min & -y_max\n",
    "quarter_cell = openmc.Cell(fill=dag_universe, region=region)\n",
    "dag_universe.auto_geom_ids = True\n",
    "\n",
    "model.geometry = openmc.Geometry([quarter_cell])\n",
    "model.export_to_model_xml()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e3141679-051f-4b5f-a815-114a23ebe1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.basis = 'xy'\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f4edc4-12c9-4780-8186-e8b2b9cde8ae",
   "metadata": {},
   "source": [
    "Any of the geometry modification options for the CSG cell apply just as they would for a geometry with CAD parts. We can just as easily take this one-quarter symmetric section and turn it into a one-eigth symmetric section by adding a Z plane to the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4040a248-894d-4670-8a3e-2ce8300976cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_min = openmc.ZPlane(-0.1, boundary_type='reflective')\n",
    "quarter_cell.region &= +z_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "30803575-d73f-4fbf-b146-f9a1ae445824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.export_to_model_xml()\n",
    "p.basis = 'xz'\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219cca19-f4d8-42db-9a5c-e756dd6d145b",
   "metadata": {},
   "source": [
    "We'll create a new tally that only covers this region of the geometry; and, same as before, we'll tally a flux in the blanket region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "81186212-884c-4fc3-bdba-1edc2c0f9485",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = openmc.RegularMesh()\n",
    "mesh.lower_left = [0.0,  0.0, 0.0]\n",
    "mesh.upper_right = [500, 500, 500]\n",
    "mesh.dimension = (200, 200, 200)\n",
    "\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "\n",
    "material_filter = openmc.MaterialFilter([mat_blanket])\n",
    "\n",
    "flux_tally = openmc.Tally(name='blanket flux')\n",
    "flux_tally.filters = [mesh_filter, material_filter]\n",
    "flux_tally.scores = ['flux']\n",
    "\n",
    "model.tallies = [flux_tally]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c9d5ab1e-bb6d-40ea-8467-89833fea9d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | c7c39e10c249c7c8eec7b2b69438cfddaa0c6b27\n",
      "       Date/Time | 2023-06-01 16:14:54\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file paramak_with_graveyard.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 4.6496e-310\n",
      "Building acceleration data structures...\n",
      " Reading Cu63 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu63.h5\n",
      " Reading Cu65 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu65.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading H2 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H2.h5\n",
      " Reading W180 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W180.h5\n",
      " Reading W182 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W182.h5\n",
      " Reading W183 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W183.h5\n",
      " Reading W184 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W184.h5\n",
      " Reading W186 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W186.h5\n",
      " Reading Fe54 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe58.h5\n",
      " Reading Pb204 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb204.h5\n",
      " Reading Pb206 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb206.h5\n",
      " Reading Pb207 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb207.h5\n",
      " Reading Pb208 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb208.h5\n",
      " Reading Li6 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li6.h5\n",
      " Reading Li7 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li7.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for H1\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.0349e+00 seconds\n",
      "   Reading cross sections          = 8.7368e-01 seconds\n",
      " Total time in simulation          = 2.1205e+01 seconds\n",
      "   Time in transport only          = 2.0970e+01 seconds\n",
      "   Time in active batches          = 2.1205e+01 seconds\n",
      "   Time accumulating tallies       = 1.2085e-01 seconds\n",
      "   Time writing statepoints        = 1.1389e-01 seconds\n",
      " Total time for finalization       = 5.5996e+00 seconds\n",
      " Total time elapsed                = 2.7907e+01 seconds\n",
      " Calculation Rate (active)         = 4715.78 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.17531 +/- 0.00175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.export_to_model_xml()\n",
    "sp_filename = model.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cc7ddd-b74d-47a0-9f81-f4aa8ed26521",
   "metadata": {},
   "source": [
    "When veiwing a X-Z slice of this image at the correct depth, we can see the rectangular coil regions clipped out of the mesh due to the combination of filters applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "887320d5-3fe0-4570-bfe8-ed920111ff79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0f7ced21c0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with openmc.StatePoint(sp_filename) as sp:\n",
    "    flux_tally = sp.get_tally(name='blanket flux')\n",
    "\n",
    "flux_mean = flux_tally.mean.reshape(*mesh.dimension)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(flux_mean[:, 100, :], origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c97a8e-41e6-474e-ae65-04cec9336770",
   "metadata": {},
   "source": [
    "Now we'll replicate this DAGMC unvierse and create a full 360 degree model. We can do this by creating two cells, one for each half of the tokamak. We then fill each cell with the DAGMC universe and rotate the universe inside the cell on the negative side of the Y=0 plane to complete the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "747a6d20-a1a9-4084-be0a-269c3fa9698c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_min = openmc.XPlane(-650.0, boundary_type='vacuum')\n",
    "x_max = openmc.XPlane(650.0, boundary_type='vacuum')\n",
    "\n",
    "y_min = openmc.YPlane(-650.0, boundary_type='vacuum')\n",
    "y_max = openmc.YPlane(650.0, boundary_type='vacuum')\n",
    "\n",
    "z_min = openmc.ZPlane(-650.0, boundary_type='vacuum')\n",
    "z_max = openmc.ZPlane(650.0, boundary_type='vacuum')\n",
    "\n",
    "y0 = openmc.YPlane(0.0)\n",
    "\n",
    "c_upper = openmc.Cell(fill=dag_universe, region=+x_min & -x_max & +y0 & -y_max & +z_min & -z_max)\n",
    "c_lower = openmc.Cell(fill=dag_universe, region=+x_min & -x_max & +y_min & -y0 & +z_min & -z_max)\n",
    "c_lower.rotation = (0.0, 0.0, 180.0) # this rotation applies to the contents of the cell\n",
    "\n",
    "dag_universe.auto_geom_ids = True\n",
    "geometry = openmc.Geometry([c_upper, c_lower])\n",
    "model.geometry = geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd6fa72-dd54-4662-b897-58e4fcb83544",
   "metadata": {},
   "source": [
    "We'll plot the model one more time to convince ourselves that it's worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f62cd421-3b68-49c4-a246-0a6b86d10863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.export_to_model_xml()\n",
    "p.basis = 'xy'\n",
    "openmc.plot_inline(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ffbc4d53-3292-4521-8d84-3fe855d25a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | c7c39e10c249c7c8eec7b2b69438cfddaa0c6b27\n",
      "       Date/Time | 2023-06-01 16:15:31\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file paramak_with_graveyard.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 1.30022e-317\n",
      "Building acceleration data structures...\n",
      " Reading Cu63 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu63.h5\n",
      " Reading Cu65 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Cu65.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading H2 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H2.h5\n",
      " Reading W180 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W180.h5\n",
      " Reading W182 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W182.h5\n",
      " Reading W183 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W183.h5\n",
      " Reading W184 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W184.h5\n",
      " Reading W186 from /home/pshriwise/data/xs/openmc/nndc_hdf5/W186.h5\n",
      " Reading Fe54 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Fe58.h5\n",
      " Reading Pb204 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb204.h5\n",
      " Reading Pb206 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb206.h5\n",
      " Reading Pb207 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb207.h5\n",
      " Reading Pb208 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Pb208.h5\n",
      " Reading Li6 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li6.h5\n",
      " Reading Li7 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Li7.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for H1\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.1092e+00 seconds\n",
      "   Reading cross sections          = 7.8428e-01 seconds\n",
      " Total time in simulation          = 1.7697e+01 seconds\n",
      "   Time in transport only          = 1.7468e+01 seconds\n",
      "   Time in active batches          = 1.7697e+01 seconds\n",
      "   Time accumulating tallies       = 1.0812e-01 seconds\n",
      "   Time writing statepoints        = 1.2020e-01 seconds\n",
      " Total time for finalization       = 5.5925e+00 seconds\n",
      " Total time elapsed                = 2.4495e+01 seconds\n",
      " Calculation Rate (active)         = 5650.68 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.18495 +/- 0.00096\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sp_filename = model.run()"
   ]
  }
 ],
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